For accurate instrumentation, it is desirable to fully understand the characteristics of the devices used. With any given one of these devices, i.e., a “device-under-test” or DUT, the specific characteristics of that DUT are not fully understood. As a simplistic example, a given “47 kΩ” resistor would rarely have a value of exactly 47,000Ω. To properly understand the operation of a circuit containing such a resistor, a knowledge of the actual resistance (e.g., 46,985.42Ω) would be helpful. It should be understood that a DUT may encompass a wide range of electrical components, equipment, and systems. A typical DUT may be a filter, an amplifier, a transmitter, a receiver, or any component, group of components, circuit, module, device, system, etc.
The drift of components, filters, amplifiers, and other signal-conditioning circuitry typically limits the accuracy of electronic measurements. A measurement system may advertise a large dynamic range and very-high resolution. However, the full dynamic range and resolution may not be realizable because of errors inherent in the system. Current measurement systems have not been demonstrated to have verifiable, full-scale uncertainties of better than 0.1 percent over all types of errors.
Real-world measurement instruments tend not to be perfect. This imperfection will affect the accuracy of the resulting measurements. This accuracy is dependent upon measurement errors. Such errors may be classed as static (systematic) errors and dynamic errors.
Static errors are repeatable, time-invariant system errors. That is, static errors do not vary over time. Static errors result from the nonideal aspects of a system. These errors are repeatable as long as no changes are made to the system. Static errors include directivity errors, source-mismatch errors, load-mismatch errors, reflection and transmission tracking errors, isolation or cross-talk errors, etc.
Static errors may be reduced through the use of precision components and circuits. However, no matter how precisely a circuit is designed, there will still be some level of static error present. Since static errors are repeatable, they can be suppressed using various static error suppression techniques, such as twelve-term error modeling, known to those skilled in the art. Twelve-term error modeling, typically employed with standard network analyzers, can account for directivity errors, source-mismatch errors, load-mismatch errors, tracking errors, and isolation errors.
Before error modeling may be employed, the error coefficients of the requisite equations must be calculated by making a set of measurements on a set of known loads meeting precise standards. A sufficient number of precise standards must be used in order to determine the various error coefficients in the error model. A common static-error calibration technique is the Short, Open, Load, and Through (SOLT) technique. The SOLT calibration technique yields better than 0.1 percent accuracy for static errors. However, dynamic errors limit the actual accuracy to less than this. An alternative static-error calibration technique, also well known to those skilled in the art, is the Through, Reflection, and Load (TRL) technique. The TRL calibration technique yields significantly better static-error accuracy than the SOLT technique at the cost of calibration complexity. There are a number of other well-known static-error calibration techniques that may be used to advantage in specific instances.
However, removing a significant amount of static error achieves little if other types of errors are not also addressed. Dynamic errors are time-varying errors. That is, dynamic errors change over time, often in an unpredictable manner. Such errors may be attributable to a number of different sources. For example, component-drift, physical-device (e.g., cables, connectors, etc.) errors, phase-noise errors, random noise, etc.
Measurement systems exhibit several types of dynamic errors. Phase-noise errors and random errors, while inherently dynamic (i.e., time-variant) are special cases independently discussed hereinafter.
Component-drift errors may be either long-term or short-term. Long-term component-drift errors, however, are typically due to aging of the components, with resultant variations in component specifications.
One type of short-term component-drift error, source drift error, is usually attributable to thermal or mechanical variations within the system, and may include both amplitude and phase fluctuations of the output wave.
Another type of short-term component-drift error, receiver drift error, is associated with a data receiver. This may be due to drift in components such as amplifiers, filters, and analog-to-digital converters. Receiver-drift error may also appear as time-varying gain and phase drift in the received signals.
Thermal variations may also lead to physical expansion of passive components within the system. At high frequencies, such expansion may lead to appreciable phase errors. In applications where the DUT is located at a considerable distance from a transmitter and/or receiver, there may be a number of time-varying errors associated with the connections between the DUT and the transmitter and receiver. These may include amplitude and phase-drift errors in the amplifiers or errors associated with the modulation and demodulation circuitry. Systems in which such errors become significant include systems utilizing propagation media other than traditional cables (e.g., the atmosphere, space, the earth, railroad tracks, power transmission lines, the oceans, etc.).
Dynamic physical errors result from physical changes in the test setup. One example of a physical error is connector repeatability. As one connects and disconnects the DUT, there will be reflection and transmission errors associated with any nonrepeatability of the connectors. The severity of the connector repeatability error is related to the type of connector, the condition of the connector, and the care with which the user makes the connection.
Cable-flexure errors are another type of dynamic physical error. Cable-flexure errors appear as one moves the cables to connect or disconnect a DUT or perform a calibration. Time-variant phase errors associated with the relaxation of the cable can occur for a period of time after the cable has been flexed.
Phase noise (jitter) is directly related to the frequency stability of a signal source. In a perfect sinusoidal oscillator, all the energy would lie at a single frequency. Since oscillators are not perfect, however, the energy will be spread slightly in frequency. This results in a pedestal effect. This effect, referred to as phase noise, is more severe at higher frequencies. Phase noise is a performance-limiting factor in applications where a weak signal must be detected in the presence of a stronger, interfering signal.
Random or white noise is common in measurement systems. Random noise includes thermal noise, shot noise, and electromagnetic interference. Random noise may appear as random data errors. Traditional and well-known techniques of random error suppression utilize some form of oversampling to determine the correct data and suppress the random errors.
Calibration frequency is also a problem in conventional signal measurement systems. Typically, high-accuracy measurement systems employing manual calibration are calibrated periodically. The interval between calibrations may be hourly, daily, weekly, monthly, quarterly, or even yearly. This technique produces a steadily decreasing accuracy that progresses over the inter-calibration interval. Additionally, drift errors occurring during the inter-calibration interval are uncompensated. Such drift errors tend to accumulate. Therefore, measurements taken shortly before calibration may be suspect. Exactly how suspect such measurements may be depends upon the length of the inter-calibration interval and the amount of drift involved.
Many state-of-the-art measurement systems employ automatic-calibration techniques. Some automatic-calibration systems calibrate at specified intervals. Such systems suffer the same decreasing accuracy as manual-calibration systems.
Other automatic-calibration systems calibrate at the beginning and end of each measurement cycle. The use of frequent nonsimultaneous calibration procedures does increase overall accuracy, but may also greatly increase the cost of measurements and prevents measurements while the frequent calibration procedures are taking place.
All calibration procedures discussed above are nonsimultaneous. That is, the calibration procedures do not occur simultaneously with measurement. Nonsimultaneous calibration procedures are incapable of correcting or compensating for dynamic errors, such as component drift, occurring during measurement.
Current technology demands increasingly small operational errors. High accuracy is therefore a growing need of instrumentation users. Measurement systems utilizing simultaneous calibration are useful for applications requiring high-accuracy measurements. That is, systems are needed that calibrate themselves and measure data simultaneously. Such systems are said to employ dynamic error suppression. That is, such systems are able to compensate for dynamic (time-variant) errors by continuously calibrating themselves while simultaneously performing the requisite measurements.
As current technology drives operational frequencies higher and higher, phase noise (i.e., signal jitter) increases in importance. A definite need exists, therefore, for systems employing phase-noise error suppression. That is, for systems employing some means of compensating for signal jitter. This is especially important in polyphase-constellation communications systems where phase noise may cause misinterpretation of the signal phase point (i.e., the data) at any given instant.
Measurement systems for state-of-the-art technology also desirably compensate for random errors.